Metal-organic frameworks (MOFs) find numerous applications due to their tunable adsorption/desorption properties. This work is focused on the spectroscopic investigation of the adsorption and release of ethylene (C2H4) and 1-methylcyclopropene (1-MCP)-natural plant growth hormone and its synthetic inhibitor-in the pores of Co3(HCOO)6 (Co-FA) MOF. Using in situ diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS), we have identified the molecular-level interactions between the adsorbed molecules and Co-FA pores, as evidenced by the characteristic shifts of the vibrational modes. The significant confinement of 1-MCP in Co-FA at room temperature was demonstrated, while moderate heating enabled its temperature-controlled release. In comparison, weaker but visible ethylene adsorption of C2H4 was also demonstrated, with significant desorption readily occurring at room temperature. Validation tests on bananas confirmed the superior performance of Co-FA over alternative MOFs, providing a link between molecular-level structure and practical applications.

X-ray photodynamic therapy (X-PDT) is a versatile treatment modality that combines the power of X-ray radiation with photosensitizers to induce localized cell death in cancerous tissues. In recent years, scheelite has emerged as a promising candidate for use in X-PDT because of its unique photoluminescent properties and biocompatibility. In this study, we investigated the influence of the concentration of europium on the XEOL spectra of Eu-doped CaWO4 samples synthesized via sonification. The results show that the XEOL spectra of the synthesized samples exhibit distinct and characteristic emission bands closely associated with Eu3+ ions, suggesting the potential presence of Eu2+. These findings are important because they point toward the occurrence of 4F6 transitions at specific wavelengths within the material. Furthermore, we observed that the intensity of these emission bands was not constant but varied significantly with the concentration of europium cations within the samples.

Sulfation is a common strategy to enhance the acidity and modify the adsorption properties of metal–organic frameworks (MOFs), yet its impact on the coordination and accessibility of active sites remains unclear. In this study, we investigate two structurally related systems—sulfated UiO-66 (UiO-66-SO4) and sulfated tetragonal zirconia (S-ZrO2)—by FTIR spectroscopy with probe molecules. Isotope exchange experiments on S-ZrO2 reveal that dehydration above 250 °C induces tridentate SO4 coordination, while hydration leads to a reversible transition to a bidentate coordination mode. In UiO-66-SO4, sulfates are coordinated in a bidentate fashion to Zr6O6 clusters, significantly affecting the accessibility of Zr sites in defective pores. This coordination prevents CO adsorption but allows acetonitrile adsorption even after room temperature activation. Unlike S-ZrO2, due to its lower thermal stability, UiO-66-SO4 cannot be evacuated at high temperatures and dehydration at 250 °C does not induce tridentate coordination. The presence of H-bonded hydroxyls in UiO-66-SO4 after activation at 250 °C supports this coordination model, indicating the formation of OH-coordinated Zr sites that are inaccessible to CO but interact with stronger bases like acetonitrile. Overall, this study provides new insights into the coordination chemistry of sulfated UiO-66 and highlights that sulfation can tune acidity and adsorption in MOFs for potential catalytic and adsorption applications.

Infrared spectroscopy (IR) is a widely used technique enabling to identify specific functional groups in the molecule of interest based on their characteristic vibrational modes or the presence of a specific adsorption site based on the characteristic vibrational mode of an adsorbed probe molecule. The interpretation of an IR spectrum is generally carried out within a fingerprint paradigm by comparing the observed spectral features with the features of known references or theoretical calculations. This work demonstrates a method for extracting quantitative structural information beyond this approach by application of machine learning (ML) algorithms. Taking palladium hydride formation as an example, Pd-H pressure-composition isotherms are reconstructed using IR data collected in situ in diffuse reflectance using CO molecule as a probe. To the best of the knowledge, this is the first example of the determination of continuous structural descriptors (such as interatomic distance and stoichiometric coefficient) from the fine structure of vibrational spectra, which opens new possibilities of using IR spectra for structural analysis.

Metal nanoparticles are widely used as heterogeneous catalysts to activate adsorbed molecules and reduce the energy barrier of the reaction. Reaction product yield depends on the interplay between elementary processes: adsorption, activation, desorption, and reaction. These processes, in turn, depend on the inlet gas composition, temperature, and pressure. At a steady state, the active surface sites may be inaccessible due to adsorbed reagents. Periodic regime may thus improve the yield, but the appropriate period and waveform are not known in advance. Dynamic control should account for surface and atmospheric modifications and adjust reaction parameters according to the current state of the system and its history. In this work, we applied a reinforcement learning algorithm to control CO oxidation on a palladium catalyst. The policy gradient algorithm was trained in the theoretical environment, parametrized from experimental data. The algorithm learned to maximize the CO2 formation rate based on CO and O2 partial pressures for several successive time steps. Within a unified approach, we found optimal stationary, periodic, and nonperiodic regimes for different problem formulations and gained insight into why the dynamic regime can be preferential. In general, this work contributes to the task of popularizing the reinforcement learning approach in the field of catalytic science.

Cu-Ga-based CO2-to-methanol hydrogenation catalysts are known to display a range of catalytic performance depending on their preparation. Here, using surface organometallic chemistry, we have prepared a series of silica-supported 3-6 nm Cu1-xGaxOy nanoparticles with a range of xGa to establish how the concentration of Ga and alloy formation affect the activity. Cu is always fully metallic in this series, while Ga is partially alloyed with Cu in the core and partially oxidized on the surface. These materials display a volcano-type activity behavior, where methanol formation is promoted when xGa < 0.13-0.18 and is suppressed at higher values, indicating a poisoning of the catalysts. In situ X-ray absorption spectroscopy shows that GaOx species over promoted Cu0.93Ga0.07-SiO2 catalyst are much more redox active than those over the poisoned Cu0.77Ga0.23-SiO2. In situ infrared spectroscopy detected methoxy intermediates over the promoted Cu0.93Ga0.07-SiO2 catalyst, while no formate or methoxy species could be observed over the poisoned Cu0.77Ga0.23-SiO2. The absence of reactive intermediates and irreversible oxidation of GaOx over poisoned catalyst suggests encapsulation of Cu by GaOx shell resulting in low activity.

This study presents a highly efficient and mild method for radical hydrosilylation of alkenes. The reaction proceeds under white-light, in the presence of Mn2(CO)10 pre-catalyst and HFIP as an additive, at r.t. and under air. Under white-light, [Mn]center dot is generated, which activates the Si-H-group to form Si center dot and trigger the autocatalytic process. HFIP acts as a unique activator which enables synthesis of the products with yields close to quantitative and with anti-Markovnikov selectivity. The method is applicable to terminal alkenes, including those with O-, N- and halogen-containing functional groups, styrene and allylbenzene derivatives, etc., as well as to a wide range of alkyl-, phenyl-, siloxy- and alkoxy-containing tertiary hydrosilanes. These conditions turned out to be most efficient for hydrosilylation of gaseous reagents such as ethylene and acetylene. In both cases the products showed quantitative yield at 1 atm and at r.t. The method is easily up-scalable in batch- and flowmodes.

Unithiol, as a biologically active ligand that forms stable complexes with metals, is of significant interest and requires systematic research when designing efficient medicinal agents. The accelerated “green” synthesis of a tungsten complex with unithiol as a ligand is carried out using two methods: ultrasonic and microwave. The precursors to this reaction are an extract of Picea pungens Engelm. spruce needles as a natural source of presumably bioactive unithiol and sodium tungstate; the reaction duration is 15 min. X-ray absorption spectra (XANES) near the L3 edge of tungsten absorption for a tungsten–unithiol complex are obtained and qualitatively analyzed for the first time.

We present a study on incorporating spiropyran photoactive molecules into the UiO-66-NH2 scaffold. Initially, we modified spiropyran molecules by introducing functional groups to facilitate covalent bonding with the MOF structure. Spiropyran molecules with carboxylic groups demonstrated the ability to coordinate zirconium in defect pores of the MOF. Alternatively, the aldehyde group showed potential for forming C-N bonds with amino groups of BDC-NH2 linkers. To validate the formation of C-N bonds within the MOF scaffold, we synthesized a complex salt of spiropyran and individual BDC-NH2 linkers. DFT calculations support our conclusions. We observed that upon introducing the photoactive moiety, the UiO-66-NH2 framework exhibited photoresponse, as demonstrated by FTIR experiments. Based on experimental data and computational results, we hypothesize that both incorporation mechanisms are viable in the functionalization process. However, steric hindrances may impede the incorporation of spiropyran into the pores, leading to surface modification instead. The elucidated mechanisms hold promise for the development of photoresponsive MOF-based smart materials.

Homogeneous Pt-catalyzed hydrosilylation is an industrially important process for the synthesis of organosilanes. Reusable heterophase (biphase) Pt-catalysts can solve economic and ecological issues arising from the high cost of Pt and its irretrievable "scattering". Previously, we proposed a sustainable and convenient-to-handle heterophase Pt/EG-catalytic system (EG - ethylene glycol). In the current research heterophase Pt/EG-catalyzed hydrosilylation was transferred in a microfluidic regime that combined several advantages. A stable droplet or segmented Taylor flow with a well-defined contact area allow for the improvement of mass and heat transfer during the scaling of such a biphase and exothermic reaction, compared to batch mode. In situ Raman spectroscopy monitoring allows for fast and continuous data collection throughout the experiment and opens an opportunity for automation of conversion analysis. We demonstrate an efficient reaction of a range of reagents in the microfluidic reactor. In this work its universal components were constructed and modified using either commercially available units or via additive technologies (3D-printing). To the best of our knowledge, this is the first example of a semi-automatic recyclization device for a real heterophase catalytic process. These results open up new perspectives for scaling and automating industrially relevant heterophase hydrosilylation reactions.

The yield of reaction products depends on the interaction between processes on the catalyst surface: adsorption, activation, reaction, desorption, and others. These processes, in turn, depend on the magnitude of the flows of reaction mixtures, temperature, and pressure. Under stationary conditions, active sites on the surface can be poisoned by reaction by-products or blocked by an excess of adsorbed reactant molecules. Dynamic control of reaction parameters takes into account changes in surface properties and adjusts the temperature, flow rates, and other parameters accordingly. A reinforcement learning algorithm was applied to control the oxidation reaction of carbon monoxide CO on the surface of palladium nanoparticles. The algorithm was trained to maximize the rate of carbon dioxide production based on information about the magnitude of CO, O-2, and CO2 flows at each time step. A gradient policy algorithm with a continuous action space was chosen, and observations of the flow rates were extended over several successive time steps, which made it possible to obtain a set of non-stationary solutions. The maximum yield of the product is achieved with a periodic change in gas flows, which ensures a balance between the available adsorption sites and the concentration of activated intermediates. This methodology opens up prospects for optimizing catalytic reactions under nonstationary conditions.

Zinc(II) complexes 2, 3, and 5 with redox-amphoteric o-indophenol ligands were prepared. The molecular structures of tetracoordinate complex 2 and hexacoordinate complex 3 were determined by single-crystal X-ray diffraction. The antioxidant properties of indophenols and their complexes were studied by cyclic voltammetry (CVA) and EPR spectroscopy. Complexation of indophenols increases the oxidation potentials by more than 0.84 V and leads to the formation of stable metal-containing radicals.

Fine tuning of the material properties requires many trials and errors during the synthesis. The metal nanoparticles undergo several stages of reduction, clustering, coalescence and growth upon their formation. Resulting properties of the colloidal solution thus depend on the concentrations of the reagents, external temperature, synthesis protocol and qualification of the researcher determines the reproducibility and quality. Automatized flow systems overcome the difficulties inherent for the conventional batch approaches. Microfluidic systems represent a good alternative for the high throughput data collection. The recent advances in 3D-printing made complex topologies in microfluidic devices cheaper and easily customizable. However, channels of the cured photopolymer resin attract metal ions upon synthesis and create crystallization centers. In our work we present 3D-printed system for the noble metal nanoparticle synthesis in slugs. Alternating flows of oil and aqueous reaction mixtures prevent metal deposition on the channel walls. Elongated droplets are convenient for optical and X-ray diagnostics using conventional methods. We demonstrate the work of the system using Ag nanoparticles synthesis for machine-learning assisted tuning of the plasmon resonance frequency.

Three UiO-66 samples were prepared by solvothermal synthesis using the defect engineering approach with benzoic acid as a modulator. They were characterized by different techniques and their acidic properties were assessed by FTIR spectroscopy of adsorbed CO and CD3CN. All samples evacuated at room temperature contained bridging μ3-OH groups that interacted with both probe molecules. Evacuation at 250 °C leads to the dehydroxylation and disappearance of the μ3-OH groups. Modulator-free synthesis resulted in a material with open Zr sites. They were detected by low-temperature CO adsorption on a sample evacuated at 200 °C and by CD3CN even on a sample evacuated at RT. However, these sites were lacking in the two samples obtained with a modulator. IR and Raman spectra revealed that in these cases, the Zr4+ defect sites were saturated by benzoates, which prevented their interaction with probe molecules. Finally, the dehydroxylation of all samples produced another kind of bare Zr sites that did not interact with CO but formed complexes with acetonitrile, probably due to structural rearrangement. The results showed that FTIR spectroscopy is a powerful tool for investigating the presence and availability of acid sites in UiO-66, which is crucial for its application in adsorption and catalysis.

Zinc(II) complexes 2 , 3 , and 5 with redox-amphoteric o -indophenol ligands were prepared. The molecular structures of tetracoordinate complex 2 and hexacoordinate complex 3 were determined by single-crystal X-ray diffraction. The antioxidant properties of indophenols and their complexes were studied by cyclic voltammetry (CVA) and EPR spectroscopy. Complexation of indophenols increases the oxidation potentials by more than 0.84 V and leads to the formation of stable metal-containing radicals.

The yield of reaction products depends on the interaction between processes on the catalyst surface: adsorption, activation, reaction, desorption, and others. These processes, in turn, depend on the magnitude of the flows of reaction mixtures, temperature, and pressure. Under stationary conditions, active sites on the surface can be poisoned by reaction by-products or blocked by an excess of adsorbed reactant molecules. Dynamic control of reaction parameters takes into account changes in surface properties and adjusts temperature, flow rates and other parameters accordingly. A reinforcement learning algorithm was applied to control the oxidation reaction of carbon monoxide CO on the surface of palladium nanoparticles. The algorithm was trained to maximize the rate of carbon dioxide production based on information about the magnitude of CO, O2 and CO2 fluxes at each time step. A gradient policy algorithm with a continuous action space was chosen, and observations of the flow rates were extended over several successive time steps, which made it possible to obtain a set of non-stationary solutions. The maximum yield of the product is achieved with a periodic change in gas flows, which ensures a balance between the available adsorption sites and the concentration of activated intermediates. This methodology opens up prospects for optimizing catalytic reactions under nonstationary conditions.

This manuscript presents a study of the applicability of machine learning methods for the problem of binding energy approximation of carbon monoxide adsorbates on the surface of a palladium nanoparticle. Machine learning algorithms were trained using a set of structures that represent models of CO interaction with different parts of the Pd55 nanocluster with a variable distance from the molecule to the surface, for which the energy was calculated using the density functional theory methods. For structures that make up a training set, the radial distribution functions were calculated. Using these functions and their segments as descriptors, the effectiveness of various machine learning algorithms, such as “gradient boosting,” “ridge regression,” “extra trees,” and “support vector machines” for calculating the binding energy, was tested. Based on three different metrics, it was found that the error in determining the binding energy was the smallest when using the “support vector machines”: the mean absolute error was 0.093 eV. The efficiency of various individual sections of the distribution function used as descriptors was compared. It was found that it is crucial to take into account the part of the radial distribution function from 1.5 to 2.5 Å for a correct approximation of the energy.

Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface–adsorbate interaction would help the reliable description and interpretation of experimental data. In this work, we applied Machine Learning (ML) algorithms for the task of adsorption-energy approximation for CO on Pd nanoclusters. Due to a high dependency of binding energy from the nature of the adsorbing site and its local coordination, we tested several structural descriptors for the ML algorithm, including mean Pd–C distances, coordination numbers (CN) and generalized coordination numbers (GCN), radial distribution functions (RDF), and angular distribution functions (ADF). To avoid overtraining and to probe the most relevant positions above the metal surface, we utilized the adaptive sampling methodology for guiding the ab initio Density Functional Theory (DFT) calculations. The support vector machines (SVM) and Extra Trees algorithms provided the best approximation quality and mean absolute error in energy prediction up to 0.12 eV. Based on the developed potential, we constructed an energy-surface 3D map for the whole Pd55 nanocluster and extended it to new geometries, Pd79, and Pd85, not implemented in the training sample. The methodology can be easily extended to adsorption energies onto mono- and bimetallic NPs at an affordable computational cost and accuracy.

Functionalization of metal-organic frameworks (MOFs) with noble metal nanoparticles (NPs) is a challenging task. Conventional impregnation by metals often leads to agglomerates on the surface of MOF crystals. Functional groups on linkers interact with metal precursors and promote the homogeneous distribution of NPs in the pores of MOFs, but their uncontrolled localization can block channels and thus hinder mass transport. To overcome this problem, we created nucleation centers only in the defective pores of the UiO-66 MOF via the postsynthesis exchange. First, we have introduced defects into UiO-66 using benzoic acid as a modulator. Second, the modulator was exchanged for amino-benzoic acid. As a result, amino groups have decorated mainly the defective pores and attracted the Pd precursor after impregnation. The interaction of the metal precursor with amino groups and the growth of NPs were monitored by in situ infrared spectroscopy. Three processes were distinguished: the gaseous HCl release, NH2 reactivation, and growth of extended Pd surfaces. Uniform Pd NPs were located in the pores because of the homogeneous distribution of the precursor and pore diffusion-limited nucleation rate. Our work demonstrates an alternative approach of controlled Pd incorporation into UiO-66 that is of great importance for the rational design of heterogeneous catalysts.